A Comparative Study of Genetic Algorithm with the KNN Evolutionary Optimization Algorithm in Data Mining

Abstract: Evolutionary optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums. This paper proposes the comparative study of inspired algorithms like Stem Cells Algorithm (SCA), Ant Colony Optimization (ACO) algorithm with the K-nearest neighbor algorithm (KNN) to reduce the local minima by using benchmark functions in data mining. Keywords: Evolutionary inspired optimization algorithm, local minima, benchmark functions.